As the amount of information collected in an online environment grows, individuals are increasingly protective of providing various forms of information. Accordingly, differential privacy has become an important consideration for providers that aggregate online information. In a crowdsourced, client/server environment, local differential privacy introduces randomness into user data prior to a client sharing the user data with a server. A server can learn from the aggregation of the crowdsourced data of all clients, but the server cannot learn the data provided by any particular client. As more user information is gathered, general patterns begin to emerge, which can inform and enhance the user experience. Accordingly, differential privacy provides insights from large datasets, but with a mathematical proof that information about a single individual remains private.
When employing local differential privacy, a client device is required to perform various operations to create privatized data. Client device operations may include encoding data, which in certain instances (e.g. where the order of random values reaches the thousands or millions) may be resource intensive in terms of computational cost and transmission bandwidth. In addition, a server must perform correspondingly intensive operations to process the privatized data. Accordingly, there is continued need to provide efficient mechanisms for achieving local differential privacy of user data.